A survey of few-shot knowledge graph completion

被引:0
|
作者
Zhang, Chaoqin [1 ]
Li, Ting [1 ]
Yin, Yifeng [1 ]
Ma, Jiangtao [1 ]
Gan, Yong [2 ]
Zhang, Yanhua [1 ]
Qiao, Yaqiong [3 ,4 ]
机构
[1] Zhengzhou Univ Light Ind, Sch Comp & Commun Engn, Zhengzhou 450002, Peoples R China
[2] Zhengzhou Inst Technol, Zhengzhou, Peoples R China
[3] North China Univ Water Resources & Elect Power, Sch Informat Engn, Zhengzhou, Peoples R China
[4] Henan Key Lab Cyberspace Situat Awareness, Zhengzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Knowledge graph; few-shot learning; knowledge graph completion; temporal knowledge graph completion;
D O I
10.3233/JIFS-232260
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the continuous development of knowledge graph completion (KGC) technology, the problem of few-shot knowledge graph completion (FKGC) is becoming increasingly prominent. Traditional methods for KGC are not effective in addressing this problem due to the lack of sufficient data samples. Therefore, completing the task of knowledge graph with few-shot data has become an urgent issue that needs to be addressed and solved. This paper first presents a concise introduction to FKGC, which covers relevant definitions and highlights the advantages of FKGC techniques. We then categorize FKGC methods into meta-learning-based, metric-based, and graph neural network-based methods, and analyze the unique characteristics of each model. We also introduced the research on FKGC in a specific domain - Temporal Knowledge Graph Completion (TKGC). Subsequently, we summarized the commonly used datasets and evaluation metrics in existing methods and evaluated the completion performance of different models in TKGC. Finally, we presented the challenges faced by FKGC and provided directions for future research.
引用
收藏
页码:6127 / 6143
页数:17
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